On a typical day, as many as 25,000 aircraft flights occur within the United States, and several times that number occur throughout the world. Most of these flights are safe. A few might exhibit safety issues. Many aircraft are equipped with instrumentation that collects from a few dozen parameters to a few thousand parameters every second for the full duration of the flight. These types of data have long been used for crash investigations, but can also be used for routine monitoring of flight operations. The subject invention relates to the latter activity. This provides an opportunity to analyze this data to identify portions of flights that exhibit safety issues. Aviation experts review these flights and recommend appropriate actions as a result.
Flight data, recorded during aircraft flight, consist of a series of parameter values. Each parameter describes a particular aspect of flight. Some parameters relate to continuous data such as altitude and airspeed. Other parameters assume a relatively small number of discrete values (e.g., two or three), such as thrust reverser position, flight guidance or autopilot command mode. Parameter measurements are usually made once per second although they may be recorded more or less frequently. Hundreds or even thousands of parameters may be collected for each second of an entire flight. These data are recorded for thousands of flights. The resulting data for an even modest size set of flights are voluminous.
These types of data have long been used for crash investigations but can also be used for routine monitoring of flight operations. The subject invention relates to the latter activity. The features of interest in routinely monitored flight data include specified exceedences (excessive speed, g-forces, and other characteristics that differ from standard operating procedures), unusual events, and statistical patterns and/or trends.
Digital flight data are passed through a series of processing steps to convert the massive quantities of raw data, collected during routine flight operations, into useful information such as that described above. The raw data are progressively reduced using both deterministic and statistical methods. In the final stages of processing, statistical methods are used to identify flights to be reviewed by aviation experts, who infer key safety and operational information about the flights described in the data. These flight data processing methods are imbedded in software.
Conventional methods of finding anomalous flights in bodies of digital flight data require users to pre-define the operational patterns that constitute unwanted performances. This can be a hit-or-miss process, requiring the experience and knowledge of experts in aviation operations, and it only identifies occurrences that specifically match the pre-defined condition. A conventional flight data analysis tool will find the patterns it is told to look for in flight data, but the tool is blind to newly emergent patterns for which the tool has not been programmed to look. The invention overcomes this deficiency because it does not require any pre-specification of what to look for in bodies of flight data.
Most flights are typical and exhibit no safety issues. A very few flights stand out as atypical based values displayed by the data. These flights may be atypical due to one flight parameter being very unusual or multiple parameters being moderately unusual. It turns out that these unusual flights often exhibit safety issues and thus are of interest to identify and refer to aviation safety experts for review. Additionally, these atypical flights might display safety issues in a manner never envisioned by safety experts; hence impossible to find using pre-defined exceedences as done by the current state of the practice.
What is needed is a system for identifying and displaying results for atypical phases of aircraft flights that provides individual and collective information on the flight phases that are determined to be atypical according to one or more criteria. Preferably, the display system should allow graphic and tabular display and comparison of relevant details that contribute to a specified phase atypicality and collective phase information for which atypical behavior occurs.